Epävarmuuden hallinta ohjausjärjestelmien ja systemaattisen riskienhallinnan avulla

Tutkimuksen tavoitteena on selvittää, miten yritykset hallitsevat liiketoimintaan liittyvää epävarmuutta ohjausjärjestelmien ja systemaattisen riskienhallinnan avulla. Ohjausjärjestelmissä keskitytään erityisesti taloudellisen kehityksen ja asetettujen tavoitteiden seurantaan käytettäviin järjestelmiin ja menetelmiin. Tutkimusmenetel-mänä käytetään kvalitatiivista analyysiä ja tutkimuksen empiirisen osan aineisto on kerätty julkisesti saatavilla olevasta informaatiosta. Tutkimuksessa käytetään poikkileikkausaineistoa. Aineisto koostuu 43 yrityksestä ja tiedot aineistoon on kerätty vuonna 2005 julkaistusta informaatiosta. Tutkimustulosten mukaan yritysten taloudellista kehitystä seurattaessa selvästi merkittävimmässä roolissa näyttää olevan budjetti. Budjetin ohella tutkitut yritykset ilmoittavat käyttävänsä eniten rullaavaa ennustamista, tuloskorttia sekä taloudellista lisäarvoa asetettujen tavoitteiden toteutumisen seurantaan. Toimialoja vertailtaessa tutkimustulokset osoittavat, että informaatioteknologiateollisuuden yritykset käyttävät laajempaa informaatiota ja suosivat tarkempaa informaatiota tuottavia ohjaus-menetelmiä, johtuen toimialalla vaikuttavista epävarmuustekijöistä ja nopeasti muuttuvasta liiketoimintaympäristöstä. Kohdeyritykset integroivat riskienhallinnan tyypillisesti johtamisjärjestelmään ja riskienhallinnan avulla tavoitellaan muun muassa asetettujen tavoitteiden saavuttamisen varmistamista.

Estimointi ja taloudellinen ennustaminen IT-järjestelmien toimitusprojekteissa

Nykyään projektityö on erittäin yleistä katsomatta toimialaan tai yritysten kokoon. Projektiestimointi on olennainen osa onnistuneen hankkeen läpiviennissä. Työssä käsitellään projektin alkuvaiheessa tapahtuvaan arviontiprosessiin liittyvää teknistä ja taloudellista estimointia. Työssä perehdytään työmäärien estimointimenetelmiin sekä taloudellisen ennusteen rakentamiseen.

Il crowdfunding: analisi delle previsioni finanziarie

La tesi tratta dell'equity-based crowdfunding e del ruolo che le previsioni economico-finanziarie svolgono all'interno delle campagne. Ci si chiede se anche per questo modello di crowdfunding, per cui investire implica sottoscrivere al capitale di rischio dell'impresa, gli aspetti economico-finanziari possano passare in secondo piano. Lo studio è stato condotto su un unico campione di 114 progetti (già finanziati) estratto da una delle principali piattaforme di equity crowdfunding: Crowdcube.

Energy forward price prediction with a hybrid adaptive model

This paper presents a forecasting technique for forward electricity/gas prices, one day ahead. This technique combines a Kalman filter (KF) and a generalised autoregressive conditional heteroschedasticity (GARCH) model (often used in financial forecasting). The GARCH model is used to compute next value of a time series. The KF updates parameters of the GARCH model when the new observation is available. This technique is applied to real data from the UK energy markets to evaluate its performance. The results show that the forecasting accuracy is improved significantly by using this hybrid model. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads.

Forecasting financial weather - can we foresee market sentiment? Spectrum of stock price behavior - NYSE case study

The desire to create a statistical or mathematical model, which would allow predicting the future changes in stock prices, was born many years ago. Economists and mathematicians are trying to solve this task by applying statistical analysis and physical laws, but there are still no satisfactory results. The main reason for this is that a stock exchange is a non-stationary, unstable and complex system, which is influenced by many factors. In this thesis the New York Stock Exchange was considered as the system to be explored. A topological analysis, basic statistical tools and singular value decomposition were conducted for understanding the behavior of the market. Two methods for normalization of initial daily closure prices by Dow Jones and S&P500 were introduced and applied for further analysis. As a result, some unexpected features were identified, such as a shape of distribution of correlation matrix, a bulk of which is shifted to the right hand side with respect to zero. Also non-ergodicity of NYSE was confirmed graphically. It was shown, that singular vectors differ from each other by a constant factor. There are for certain results no clear conclusions from this work, but it creates a good basis for the further analysis of market topology.

Forecasting financial weather - can we foresee market sentiment? Quantitative analysis of nodal prices in the New Zealand Electricity Spot Market

Time series of hourly electricity spot prices have peculiar properties. Electricity is by its nature difficult to store and has to be available on demand. There are many reasons for wanting to understand correlations in price movements, e.g. risk management purposes. The entire analysis carried out in this thesis has been applied to the New Zealand nodal electricity prices: offer prices (from 29 May 2002 to 31 March 2009) and final prices (from 1 January 1999 to 31 March 2009). In this paper, such natural factors as location of the node and generation type in the node that effects the correlation between nodal prices have been reviewed. It was noticed that the geographical factor affects the correlation between nodes more than others. Therefore, the visualisation of correlated nodes was done. However, for the offer prices the clear separation of correlated and not correlated nodes was not obtained. Finally, it was concluded that location factor most strongly affects correlation of electricity nodal prices; problems in visualisation probably associated with power losses when the power is transmitted over long distance.

Forecasting economic and financial time series with non-linear models

In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among non-linear forecasting models for economic and financial time series. We review theoretical and empirical issues, including predictive density, interval and point evaluation and model selection, loss functions, data-mining, and aggregation. In addition, we argue that although the evidence in favor of constructing forecasts using non-linear models is rather sparse, there is reason to be optimistic. However, much remains to be done. Finally, we outline a variety of topics for future research, and discuss a number of areas which have received considerable attention in the recent literature, but where many questions remain.

Forecasting real estate returns using financial spreads

This paper examines the predictability of real estate asset returns using a number of time series techniques. A vector autoregressive model, which incorporates financial spreads, is able to improve upon the out of sample forecasting performance of univariate time series models at a short forecasting horizon. However, as the forecasting horizon increases, the explanatory power of such models is reduced, so that returns on real estate assets are best forecast using the long term mean of the series. In the case of indirect property returns, such short-term forecasts can be turned into a trading rule that can generate excess returns over a buy-and-hold strategy gross of transactions costs, although none of the trading rules developed could cover the associated transactions costs. It is therefore concluded that such forecastability is entirely consistent with stock market efficiency.

Forecasting daily volatility using high frequency financial data

Aiming at empirical findings, this work focuses on applying the HEAVY model for daily volatility with financial data from the Brazilian market. Quite similar to GARCH, this model seeks to harness high frequency data in order to achieve its objectives. Four variations of it were then implemented and their fit compared to GARCH equivalents, using metrics present in the literature. Results suggest that, in such a market, HEAVY does seem to specify daily volatility better, but not necessarily produces better predictions for it, what is, normally, the ultimate goal. The dataset used in this work consists of intraday trades of U.S. Dollar and Ibovespa future contracts from BM&FBovespa.

Pensions : a report from the Commission on Government Forecasting & Accountability on the financial condition of the state of Illinois retirement systems as of June 30, 2008.

Examines the financial status of the various public employee retirement systems in Illinois.

A válság mint negatív információ és bizonytalansági tényező. A válság hatása az egy részvényre jutó nyereség-előrejelzésekre (The financial crisis as negative information and a factor of uncertainty. Analysing the EPS forecasting error in Hungary and Austria)

A szerző a 2008-ban kezdődött gazdasági világválság hatását vizsgálja az egy részvényre jutó nyereség előrejelzésének hibájára. Számos publikáció bizonyította, hogy az elemzők a tényértékeknél szisztematikusan kedvezőbb tervértéket adnak meg az egy részvényre jutó előrejelzéseikben. Más vizsgálatok azt igazolták, hogy az egy részvényre jutó előrejelzési hiba bizonytalan környezetben növekszik, míg arra is számos bizonyítékot lehet találni, hogy a negatív hírek hatását az elemzők alulsúlyozzák. A gazdasági világválság miatt az elemzőknek számtalan negatív hírt kellett figyelembe venniük az előrejelzések készítésekor, továbbá a válság az egész gazdaságban jelentősen növelte a bizonytalanságot. A szerző azt vizsgálja, hogy miként hatott a gazdasági világválság az egy részvényre jutó nyereség- előrejelzés hibájára, megkülönböztetve azt az időszakot, amíg a válság negatív hír volt, attól, amikor már hatásaként jelentősen megnőtt a bizonytalanság. _____ The author investigated the impact of the financial crisis that started in 2008 on the forecasting error for earnings per share. There is plentiful evidence from the 1980s that analysts give systematically more favourable values in their earnings per share (EPS) forecasts than reality, i.e. they are generally optimistic. Other investigations have supported the idea that the EPS forecasting error is greater under uncertain environmental circumstances, while other researchers prove that the analysts under-react to the negative information in their forecasts. The financial crisis brought a myriad of negative information for analysts to consider in such forecasts, while also increasing the level of uncertainty for the entire economy. The article investigates the impact of the financial crisis on the EPS forecasting error, distinguishing the period when the crisis gave merely negative information, from the one when its effect of uncertainty was significantly increased over the entire economy.